Systems and methods exist to measure electrical activity and detect brain abnormalities based on the measured activity. An electroencephalogram (“EEG”) detects electrical activity of the brain using electrodes placed on or near an individual's scalp and forehead. The EEG typically measures brain wave activity across a range of frequencies, from one or an array of electrodes placed at standardized locations on the head or scalp and forehead. For example, 19 different sites are often used to detect the activity of underlying regions of the brain. The most common method to define these sites is called the International 10/20 Electrode Placement System, but other positions may be selected.
Quantitative EEG (“qEEG”) involves digital analysis of the EEG data, usually represented as a frequency spectrum evaluating the amount of activity of detected signals at the various frequencies represented in the EEG data Normative values as a function of age have been established for the activity spectrum characteristics for each electrode position and are known to have high sensitivity and specificity to abnormality and to be independent of race, socio-economic status or ethnicity. Deviations from these normative values may be used to identify profiles of abnormalities which can describe different categories of brain dysfunction. Studies utilizing qEEG have reported a clear relationship between the degree of cognitive decline in aging persons and the magnitude of qEEG abnormalities.
Systems and methods exist to measure the magneto elcephalogram (MEG) electromagnetic activity or magnetic fields arising from the flow of electrical currents within the brain which are proportional to the electroencephalographic activity. These magnetic fields can be used to detect brain abnormalities based on the measured activity. These magnetic fields are detected using data derived from superconductive quantum interference devices (SQUIDS) to detect spontaneous and or evoked electromagnetic activity correlated with brain electrical activity (EEG and or ERP) of an individual using one or more SQUID or MEG sensors placed near the scalp so as to detect the activity from selected regions of the brain.
Quantitative MEG (“qMEG”) involves digital analysis of the MEG data, usually represented as a frequency spectrum evaluating the amount of activity of detected signals at the various frequencies represented in the MEG data Normative values as a function of age have not been established for the activity spectrum, but it is expected that such can be described and will be closely related to the qEEG normative data with respect to sensitivity and specificity to abnormality and to be independent of race, socio-economic status or ethnicity since the essentially the same neuroanatomical systems are responsible for generating both the MEG and the EEG signals. Using control data with the MEG similar differences have been described between individuals with normal and abnormal brain function as with the EEG in different categories of brain dysfunction.
An event-related potential (“ERP”) is a recording of a neuroelectrical response of the brain to an event or stimulus. Changes in ERPs recorded from electrodes using EEG recording materials have been found to provide objective evidence of brain dysfunction relating to cognitive decline. Non-invasive ERP tests are available to measure brain activity during cognitive processing. ERP tests such as mismatch negativity (MMN) and P300 have been used to assess cognitive decline in a variety of areas including, for example, attention and memory. Similar tests may be performed using a variety of sensory events and paradigms. One such ERP test (P300) may include detecting a difference between an ERP elicited by a “common” (expected) event that occurs often in a sequence of repeated stimuli and ERP elicited by a rare (unexpected) event that is occasionally interspersed among the common (expected) ones. Another such test compares ERP's corresponding to pairs of stimuli where the first and second members of a pair are the same (‘match’) and where the second member of the pair is different from the first (‘mismatch’). This type of test is known as MMN or mismatch negativity. Cognitive decline has been reported to correlate with the decrease in amplitude or disappearance and lengthening in latency of particular ERP components elicited by these tests. Another ERP test is known as the mid-latency auditory evoked response (MLAER) which examines the waveshape of electrical activity of the brain in response to tones or auditory clicks repeated for example at about 9/second. In the latency interval from about 6-8 milliseconds after stimulus delivery to approximately 50 milliseconds after stimulus delivery, certain components (e.g., Na and Pb) are known to be correlated with the storage of memory representing interactions between the midbrain and the cortex. Cognitive decline and memory deficit have been reported to correlate with the increase in amplitude or disappearance or lengthening in latency of particular ERP components elicited by these tests. The clinical assessment of the cognitive and functional capacity of individuals for their diagnoses and “staging” (to access the degree of impairment) may also rely upon neurocognitive (“NC”) tests. NC tests have poor test-retest reliability in certain patient populations and are sometimes inaccurate or not useful with certain individuals because they depend to a large extent on the competence and experience of the person performing the assessment, as well as the language proficiency, educational attainment, active cooperation, and intelligence level of the individual. NC or other behavioral testing may not be useful where, for example, if the individual is depressed (or has other co-morbid disorders), disinterested in the test, has a short attention span or a low educational level. Abnormal performance on neurocognitive or behavioral measures does not necessarily reflect changes in the brain and at best are an indirect result of actual brain changes resulting from dementia. Furthermore, in people without signs of dementia (such as normal elderly) differences in NC tests are not sufficient to predict those persons likely to develop dementia from those who are unlikely to develop dementia.
Although methods exist to determine the existence of cognitive deterioration or decline, these methods do not enable the determination of an individual's potential for future cognitive decline. Although, the use of other imaging methods [e.g., Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET) and Single Proton Emission Computed Tomography (SPECT)] to detect abnormalities which may be predictive of future decline has been proposed, these tests are invasive, costly, lack normative data, depend on the skill of the person interpreting the data, can not be readily repeated at regular intervals and require special installations of large non-portable equipment.
Predictoralgorithms are classifier functions which mathematically compute the probability that a set of features [such as those extracted from the qEEG and or qMEG and or ERP described in this application] are likely to belong to an individual from a particular group. Such algorithms may include, alone or in combination, multiple discriminant functions, regressions, cluster analyses or other classifier programs.